{ "_id": "6a1c05041d7bb097a0a2a63e", "Package": "ODS", "Type": "Package", "Title": "Statistical Methods for Outcome-Dependent Sampling Designs", "Version": "0.2.0", "Authors@R": "c(person(\"Yinghao\", \"Pan\", role = c(\"aut\", \"cre\"),\nemail = \"ypan8@uncc.edu\"),\nperson(\"Haibo\", \"Zhou\", role = \"aut\",\nemail = \"zhou@bios.unc.edu\"),\nperson(\"Mark\", \"Weaver\", role = \"aut\"),\nperson(\"Guoyou\", \"Qin\", role = \"aut\"),\nperson(\"Jianwen\", \"Cai\", role = \"aut\"))", "Author": "Yinghao Pan [aut, cre], Haibo Zhou [aut], Mark Weaver [aut],\nGuoyou Qin [aut], Jianwen Cai [aut]", "Maintainer": "Yinghao Pan ", "Description": "Outcome-dependent sampling (ODS) schemes are\ncost-effective ways to enhance study efficiency. In ODS\ndesigns, one observes the exposure/covariates with a\nprobability that depends on the outcome variable. Popular ODS\ndesigns include case-control for binary outcome, case-cohort\nfor time-to-event outcome, and continuous outcome ODS design\n(Zhou et al. 2002) .\nBecause ODS data has biased sampling nature, standard\nstatistical analysis such as linear regression will lead to\nbiases estimates of the population parameters. This package\nimplements four statistical methods related to ODS designs: (1)\nAn empirical likelihood method analyzing the primary continuous\noutcome with respect to exposure variables in continuous ODS\ndesign (Zhou et al., 2002). (2) A partial linear model\nanalyzing the primary outcome in continuous ODS design (Zhou,\nQin and Longnecker, 2011) . (3) Analyze a secondary\noutcome in continuous ODS design (Pan et al. 2018) . 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